Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uchambuzi Imara wa Matukio× | Uchambuzi wa Matukio ya Kistohastiki× | |
|---|---|---|
| Nyanja | Uigaji | Uigaji |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 1950 (foundations); 2003 (modern RDM formulation) | 1955–1980s |
| Mwanzilishi≠ | Wald, A. (minimax foundation); Lempert et al. (RDM framework) | Dantzig, G. B.; Birge, J. R.; and others in stochastic programming tradition |
| Aina≠ | Scenario-based robustness evaluation | Probabilistic scenario enumeration and evaluation |
| Chanzo asilia≠ | Wald, A. (1950). Statistical Decision Functions. Wiley, New York. link ↗ | Birge, J. R., Louveaux, F. (2011). Introduction to Stochastic Programming (2nd ed.). Springer. ISBN: 9781461402374 |
| Majina mbadala | RSA, Robust Scenario Planning, Worst-Case Scenario Analysis, Minimax Regret Scenario Analysis | Probabilistic Scenario Analysis, SSA, Stochastic What-If Analysis, Monte Carlo Scenario Analysis |
| Zinazohusiana≠ | 5 | 4 |
| Muhtasari≠ | Robust Scenario Analysis evaluates a set of candidate strategies across a structured collection of plausible future scenarios and selects the strategy that performs acceptably well — or best in the worst case — regardless of which scenario materializes. It merges scenario planning with robustness criteria such as maximin, minimax regret, or satisficing to support decisions under deep, irreducible uncertainty. | Stochastic Scenario Analysis evaluates a system or decision across multiple explicitly defined scenarios, each assigned a probability of occurrence. Unlike deterministic scenario analysis, it propagates uncertainty through probability distributions and computes expected outcomes, variance, and risk metrics across the scenario space, giving decision-makers a structured view of what could happen and how likely each outcome is. |
| ScholarGateSeti ya data ↗ |
|
|